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Igor V. Tetko
Researcher at Russian Academy of Sciences
Publications - 228
Citations - 16142
Igor V. Tetko is an academic researcher from Russian Academy of Sciences. The author has contributed to research in topics: Artificial neural network & Applicability domain. The author has an hindex of 51, co-authored 218 publications receiving 13988 citations. Previous affiliations of Igor V. Tetko include University of Portsmouth & Kazan Federal University.
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Virtual computational chemistry laboratory - design and description
Igor V. Tetko,Johann Gasteiger,Roberto Todeschini,Andrea Mauri,David J. Livingstone,Peter Ertl,Vladimir A. Palyulin,Eugene V. Radchenko,Nikolai S. Zefirov,Alexander S. Makarenko,Vsevolod Yu. Tanchuk,Volodymyr V. Prokopenko +11 more
TL;DR: The main features and statistics of the developed system, Virtual Computational Chemistry Laboratory, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis are reviewed.
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The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes
Andreas Ruepp,Alfred Zollner,Dieter Maier,Kaj Albermann,Jean Hani,Martin Mokrejs,Igor V. Tetko,Ulrich Güldener,Gertrud Mannhaupt,Martin Münsterkötter,H. Werner Mewes +10 more
TL;DR: The Functional Catalogue (FunCat), a hierarchically structured, organism-independent, flexible and scalable controlled classification system enabling the functional description of proteins from any organism, is presented.
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Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds
TL;DR: An extension of a set previously used by the CheckMol software that covers in addition heterocyclic compound classes and periodic table groups is described, which demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models.
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The Fusarium graminearum Genome Reveals a Link Between Localized Polymorphism and Pathogen Specialization
Christina A. Cuomo,Ulrich Güldener,Jin-Rong Xu,Frances Trail,B. Gillian Turgeon,Antonio Di Pietro,Jonathan D. Walton,Li-Jun Ma,Scott E. Baker,Martijn Rep,Gerhard Adam,John F. Antoniw,Thomas K. Baldwin,Sarah E. Calvo,Yueh-Long Chang,David DeCaprio,Liane R. Gale,Sante Gnerre,Rubella S. Goswami,Kim E. Hammond-Kosack,Linda J. Harris,Karen Hilburn,John C. Kennell,Scott Kroken,Jon K. Magnuson,Gertrud Mannhaupt,Evan Mauceli,Hans-Werner Mewes,Rudolf Mitterbauer,Gary J. Muehlbauer,Martin Münsterkötter,David R. Nelson,Kerry O'Donnell,Thérèse Ouellet,Weihong Qi,Hadi Quesneville,M. Isabel G. Roncero,Kye Yong Seong,Igor V. Tetko,Martin Urban,Cees Waalwijk,Todd J. Ward,Jiqiang Yao,Bruce W. Birren,H. Corby Kistler,H. Corby Kistler +45 more
TL;DR: The genome of the filamentous fungus Fusarium graminearum, a major pathogen of cultivated cereals, was sequenced and annotated and many highly polymorphic regions contained sets of genes implicated in plant-fungus interactions and were unusually divergent, with higher rates of recombination.
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Neural network studies. 1. Comparison of overfitting and overtraining
TL;DR: Application of ANN ensembles has allowed the avoidance of chance correlations and satisfactory predictions of new data have been obtained for a wide range of numbers of neurons in the hidden layer.